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Fast cross-validation of kernel fisher discriminant classifiers

An, Senjian, Liu, Wanquan and Venkatesh, Svetha 2005, Fast cross-validation of kernel fisher discriminant classifiers, in ICMLA 2005 : Proceedings of the 4th International Conference on Machine Learning and Applications, IEEE, Piscataway, N. J., pp. 22-27.

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Title Fast cross-validation of kernel fisher discriminant classifiers
Author(s) An, Senjian
Liu, Wanquan
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Machine Learning and Applications (4th : 2005 : Los Angeles, Calif.)
Conference location Los Angeles, Calif.
Conference dates 15-17 Dec. 2005
Title of proceedings ICMLA 2005 : Proceedings of the 4th International Conference on Machine Learning and Applications
Editor(s) [Unknown]
Publication date 2005
Conference series International Conference on Machine Learning and Applications
Start page 22
End page 27
Total pages 6
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) algorithms
computational geometry
linear systems
matrix algebra
numerical methods
problem solving
Summary Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.
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ISBN 0769524958
9780769524955
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2005, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044613

Document type: Conference Paper
Collections: School of Information Technology
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